50行Python代码实现人脸检测功能

$ sudo apt-get install build-essential cmake 
$ sudo apt-get install libgtk-3-dev 
$ sudo apt-get install libboost-all-dev

$ pip install numpy 
$ pip install scipy 
$ pip install opencv-python 
$ pip install dlib

def rect_to_bb(rect): 
  x = rect.left() 
  y = rect.top() 
  w = rect.right() - x 
  h = rect.bottom() - y   
  return (x, y, w, h)

def shape_to_np(shape, dtype="int"): 
  coords = np.zeros((68, 2), dtype=dtype)   
  for i in range(0, 68): 
      coords[i] = (shape.part(i).x, shape.part(i).y)   
  return coords

def resize(image, width=1200): 
  r = width * 1.0 / image.shape[1] 
  dim = (width, int(image.shape[0] * r)) 
  resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)   
  return resized

import sys import numpy as np 
import dlib import cv2 
if len(sys.argv) < 2:   
  print "Usage: %s <image file>" % sys.argv[0] 
  sys.exit(1) 
image_file = sys.argv[1] 
detector = dlib.get_frontal_face_detector() 
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

image = cv2.imread(image_file) 
image = resize(image, width=1200) 
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
rects = detector(gray, 1)

for (i, rect) in enumerate(rects): 
  shape = predictor(gray, rect) 
  shape = shape_to_np(shape) 
  (x, y, w, h) = rect_to_bb(rect) 
  cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2) 
  cv2.putText(image, "Face #{}".format(i + 1), (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)   
for (x, y) in shape: 
      cv2.circle(image, (x, y), 2, (0, 0, 255), -1) 
cv2.imshow("Output", image) 
cv2.waitKey(0)